Graph neural networks (GNNs) have emerged as a powerful framework for analyzing and learning from structured data represented as graphs. GNNs operate directly on graphs, as opposed to conventional ...

Knowledge graphs and ontologies form the backbone of the Semantic Web by enabling the structured representation and interconnection of data across diverse domains. These frameworks allow for the ...

As the use of graph databases has grown in recent years, ever more applications of this technology involve storing, searching, and reasoning about events. In fact, many companies use this technology ...

Data-hungry AI applications are fed complex information, and that's where graph databases and knowledge graphs play a crucial role.

At a time when every enterprise looks to leverage generative artificial intelligence, data sites are turning their attention to graph databases and knowledge graphs. The global graph database market ...

How do you solve the age-old data integration issue? We addressed this in one of the first articles we wrote for this column back in 2016. It was a time when key terms and trends that dominate today's ...

Let G be a directed graph such that every edge e of G is associated with a positive integer, called the index of e. Then G is called a network graph if, at every vertex v of G, the sum of the indices ...

Carpathian Journal of Mathematics, Vol. 39, No. 1 (2023), pp. 213-230 (18 pages) The normalized distance Laplacian matrix of a connected graph G, denoted by D𝓛(G), is defined by D𝓛(G) = ...